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A Bayesian change point model to detect changes in event occurrence rates, with application to induced seismicity

International Conference on Applications of Statistics and Probability (12th : 2015 : Vancouver, B.C.)

A significant increase in earthquake occurrence rates has been observed in recent years in parts of Central and Eastern US. There is a possibility that this increased seismicity is anthropogenic and is referred to as induced seismicity. In this paper, a Bayesian change point model is implemented to evaluate whether temporal features of observed earthquakes support the hypothesis that a change in seismicity rates has occurred. This model is then used to estimate when the change is likely to have occurred. The magnitude of change is also quantified by estimating the distributions of seismicity rates before and after the change. These calculations are validated using a simulated data set with a known change point and event occurrence rates; and then applied to earthquake occurrence data for a site in Oklahoma.

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This collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.

Vancouver : University of British Columbia Library

10.14288/1.0076078

Non UBC

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/